Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f433fbab278>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f433fad2e48>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input = tf.placeholder(tf.float32,
                                (None, image_width, image_height, image_channels),
                                name='real_input')

    z_input = tf.placeholder(tf.float32,
                             (None, z_dim),
                             name='z_input')
    
    learning_rate = tf.placeholder(tf.float32, [], name='learning_rate')
    
    return (real_input, z_input, learning_rate)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = .2  #.1  #.2

    with tf.variable_scope('discriminator', reuse=reuse):
        
        # Input layer is 28x28x3
        # Convolution
        x0 = tf.layers.conv2d(images, 32, 5, strides=2, padding='same')
        x0 = tf.maximum(alpha * x0, x0)  # LeakyReLu
        # 14x14x32 now
        
        # Convolution
        x1 = tf.layers.conv2d(x0, 64, 5, strides=2, padding='same')
        x1 = tf.layers.batch_normalization(x1, training=True)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x64 now
        
        # Convolution
        x2 = tf.layers.conv2d(x1, 128, 5, strides=1, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(alpha * x2, x2)
        # 7x7x128 now
        
        # Flatten
        flat = tf.reshape(x2, (-1, 7*7*128))
        logits = tf.layers.dense(flat, 1)
        output = tf.sigmoid(logits)

        return (output, logits)



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = .2  #.1  #.2
    
    with tf.variable_scope('generator', reuse=not is_train):

        # Fully Conn
        dense = tf.layers.dense(z, 7*7*64)
        
        # Reshape
        reshape = tf.reshape(dense, (-1, 7, 7, 64))
        # 7x7x64 now
        
        # Convolution
        x0 = tf.layers.conv2d_transpose(reshape, 32, 5, strides=2, padding='same')
        x0 = tf.layers.batch_normalization(x0, training=is_train)
        x0 = tf.maximum(alpha * x0, x0)
        # 14x14x32
        
        # Convolution
        x1 = tf.layers.conv2d_transpose(x0, 16, 5, strides=2, padding='same')
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 28x28x16
        
        # Convolution (logits)
        x2 = tf.layers.conv2d_transpose(x1, out_channel_dim, 5, strides=1, padding='same')
        # 28x28x3
        
        output = tf.tanh(x2)
        
        return output

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
                    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                                            labels=tf.ones_like(d_logits_real)))
    d_loss_fake = tf.reduce_mean(
                    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                            labels=tf.zeros_like(d_logits_fake)))
    d_loss = d_loss_real + d_loss_fake
    
    g_loss = tf.reduce_mean(
                    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                            labels=tf.ones_like(d_logits_fake)))
    return (d_loss, g_loss)

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    
    g_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    d_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    
    return (d_opt, g_opt)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    
    _, img_w, img_h, img_d = data_shape
    real_input, z_input, lr = model_inputs(img_w, img_h, img_d, z_dim)
    d_loss, g_loss = model_loss(real_input, z_input, img_d)
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    tile_size = 16  #images per tile
    iteration = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                iteration += 1
                
                # TODO: Train Model
                z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                batch_images *= 2  # to expand the images range to -1. to +1. 
                _ = sess.run(d_opt, feed_dict={real_input: batch_images, z_input: z, lr:learning_rate})
                _ = sess.run(g_opt, feed_dict={z_input: z, lr:learning_rate})
                
                if iteration % 25 == 0:
                    dis_train_loss = d_loss.eval({z_input: z, real_input: batch_images})
                    gen_train_loss = g_loss.eval({z_input: z})
                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(dis_train_loss),
                          "Generator Loss: {:.4f}".format(gen_train_loss))
                
                if iteration % 100 == 0:
                    show_generator_output(sess, tile_size, z_input, img_d, data_image_mode)
        # to show the final output             
        show_generator_output(sess, tile_size, z_input, img_d, data_image_mode)
        
        

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 64  #64  #20  #5 
z_dim = 100  #255
learning_rate = .0006  # .001  #0.0001
beta1 = .5  #.6 #.7 #.8 #.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.3420... Generator Loss: 1.8149
Epoch 1/2... Discriminator Loss: 1.5766... Generator Loss: 2.0184
Epoch 1/2... Discriminator Loss: 2.6662... Generator Loss: 0.0965
Epoch 1/2... Discriminator Loss: 1.0241... Generator Loss: 1.3340
Epoch 1/2... Discriminator Loss: 0.8490... Generator Loss: 0.9801
Epoch 1/2... Discriminator Loss: 0.9166... Generator Loss: 1.1025
Epoch 1/2... Discriminator Loss: 0.6507... Generator Loss: 1.4422
Epoch 1/2... Discriminator Loss: 0.6571... Generator Loss: 1.7177
Epoch 1/2... Discriminator Loss: 0.6592... Generator Loss: 1.0282
Epoch 1/2... Discriminator Loss: 0.4444... Generator Loss: 1.4888
Epoch 1/2... Discriminator Loss: 0.4306... Generator Loss: 1.9127
Epoch 1/2... Discriminator Loss: 0.5648... Generator Loss: 1.9860
Epoch 1/2... Discriminator Loss: 1.2946... Generator Loss: 2.9501
Epoch 1/2... Discriminator Loss: 0.5929... Generator Loss: 1.7744
Epoch 1/2... Discriminator Loss: 0.6587... Generator Loss: 1.6013
Epoch 1/2... Discriminator Loss: 0.6451... Generator Loss: 1.8705
Epoch 1/2... Discriminator Loss: 1.7530... Generator Loss: 3.8339
Epoch 1/2... Discriminator Loss: 0.9392... Generator Loss: 0.7491
Epoch 1/2... Discriminator Loss: 0.8971... Generator Loss: 2.6525
Epoch 1/2... Discriminator Loss: 1.0360... Generator Loss: 0.6909
Epoch 1/2... Discriminator Loss: 0.7072... Generator Loss: 1.4244
Epoch 1/2... Discriminator Loss: 0.9207... Generator Loss: 0.7295
Epoch 1/2... Discriminator Loss: 0.8123... Generator Loss: 0.8292
Epoch 1/2... Discriminator Loss: 0.6695... Generator Loss: 1.1867
Epoch 1/2... Discriminator Loss: 0.7893... Generator Loss: 1.3759
Epoch 1/2... Discriminator Loss: 0.9865... Generator Loss: 0.6276
Epoch 1/2... Discriminator Loss: 0.7149... Generator Loss: 1.1034
Epoch 1/2... Discriminator Loss: 0.5864... Generator Loss: 1.3409
Epoch 1/2... Discriminator Loss: 0.6442... Generator Loss: 1.9959
Epoch 1/2... Discriminator Loss: 0.8163... Generator Loss: 1.3585
Epoch 1/2... Discriminator Loss: 0.6567... Generator Loss: 1.0667
Epoch 1/2... Discriminator Loss: 0.7525... Generator Loss: 0.9013
Epoch 1/2... Discriminator Loss: 0.8257... Generator Loss: 0.8818
Epoch 1/2... Discriminator Loss: 1.3879... Generator Loss: 0.4474
Epoch 1/2... Discriminator Loss: 0.4580... Generator Loss: 1.5406
Epoch 1/2... Discriminator Loss: 0.6075... Generator Loss: 1.7233
Epoch 1/2... Discriminator Loss: 0.7767... Generator Loss: 1.1540
Epoch 2/2... Discriminator Loss: 0.8109... Generator Loss: 0.9722
Epoch 2/2... Discriminator Loss: 0.7699... Generator Loss: 0.9380
Epoch 2/2... Discriminator Loss: 0.5732... Generator Loss: 1.4519
Epoch 2/2... Discriminator Loss: 1.4413... Generator Loss: 3.9203
Epoch 2/2... Discriminator Loss: 1.0628... Generator Loss: 0.7413
Epoch 2/2... Discriminator Loss: 0.7284... Generator Loss: 1.1462
Epoch 2/2... Discriminator Loss: 0.8325... Generator Loss: 0.8445
Epoch 2/2... Discriminator Loss: 0.5526... Generator Loss: 1.3488
Epoch 2/2... Discriminator Loss: 0.9296... Generator Loss: 0.7607
Epoch 2/2... Discriminator Loss: 0.7230... Generator Loss: 0.9594
Epoch 2/2... Discriminator Loss: 0.5591... Generator Loss: 1.3625
Epoch 2/2... Discriminator Loss: 0.4694... Generator Loss: 1.3393
Epoch 2/2... Discriminator Loss: 0.6347... Generator Loss: 1.6088
Epoch 2/2... Discriminator Loss: 0.4050... Generator Loss: 1.7862
Epoch 2/2... Discriminator Loss: 0.3635... Generator Loss: 1.9729
Epoch 2/2... Discriminator Loss: 0.9688... Generator Loss: 0.7644
Epoch 2/2... Discriminator Loss: 0.6267... Generator Loss: 2.8826
Epoch 2/2... Discriminator Loss: 0.8980... Generator Loss: 0.7693
Epoch 2/2... Discriminator Loss: 0.4278... Generator Loss: 1.5551
Epoch 2/2... Discriminator Loss: 0.3394... Generator Loss: 1.7425
Epoch 2/2... Discriminator Loss: 0.5269... Generator Loss: 1.3280
Epoch 2/2... Discriminator Loss: 0.5871... Generator Loss: 1.1670
Epoch 2/2... Discriminator Loss: 0.7110... Generator Loss: 0.9406
Epoch 2/2... Discriminator Loss: 0.9426... Generator Loss: 0.8430
Epoch 2/2... Discriminator Loss: 0.6998... Generator Loss: 1.0439
Epoch 2/2... Discriminator Loss: 0.4891... Generator Loss: 2.1517
Epoch 2/2... Discriminator Loss: 0.5081... Generator Loss: 1.6162
Epoch 2/2... Discriminator Loss: 0.6027... Generator Loss: 1.2937
Epoch 2/2... Discriminator Loss: 0.4005... Generator Loss: 1.6445
Epoch 2/2... Discriminator Loss: 0.4263... Generator Loss: 1.8423
Epoch 2/2... Discriminator Loss: 0.7683... Generator Loss: 1.0215
Epoch 2/2... Discriminator Loss: 0.4858... Generator Loss: 1.3321
Epoch 2/2... Discriminator Loss: 0.3128... Generator Loss: 2.0159
Epoch 2/2... Discriminator Loss: 0.2717... Generator Loss: 2.1298
Epoch 2/2... Discriminator Loss: 0.6629... Generator Loss: 1.2529
Epoch 2/2... Discriminator Loss: 2.2706... Generator Loss: 4.7375
Epoch 2/2... Discriminator Loss: 0.5807... Generator Loss: 1.3209

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 64  #128  #64  #16  #64  #20  #5 
z_dim = 100  #128 # 200 #255
learning_rate = .0008  #.001  #.0001
beta1 = .7  #.45 #.6 #.8 #.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.6681... Generator Loss: 2.3778
Epoch 1/1... Discriminator Loss: 0.3147... Generator Loss: 2.3498
Epoch 1/1... Discriminator Loss: 0.1424... Generator Loss: 3.5221
Epoch 1/1... Discriminator Loss: 0.4233... Generator Loss: 1.7184
Epoch 1/1... Discriminator Loss: 0.1500... Generator Loss: 4.3746
Epoch 1/1... Discriminator Loss: 0.1175... Generator Loss: 5.5534
Epoch 1/1... Discriminator Loss: 0.5416... Generator Loss: 4.0922
Epoch 1/1... Discriminator Loss: 0.4906... Generator Loss: 4.6674
Epoch 1/1... Discriminator Loss: 0.9182... Generator Loss: 4.9459
Epoch 1/1... Discriminator Loss: 0.9726... Generator Loss: 1.0492
Epoch 1/1... Discriminator Loss: 0.4539... Generator Loss: 2.1943
Epoch 1/1... Discriminator Loss: 0.8587... Generator Loss: 3.5871
Epoch 1/1... Discriminator Loss: 0.8301... Generator Loss: 1.2861
Epoch 1/1... Discriminator Loss: 1.2641... Generator Loss: 0.8263
Epoch 1/1... Discriminator Loss: 0.3103... Generator Loss: 2.5887
Epoch 1/1... Discriminator Loss: 0.5390... Generator Loss: 4.1227
Epoch 1/1... Discriminator Loss: 0.5706... Generator Loss: 1.6717
Epoch 1/1... Discriminator Loss: 0.7283... Generator Loss: 1.5757
Epoch 1/1... Discriminator Loss: 0.7404... Generator Loss: 1.6024
Epoch 1/1... Discriminator Loss: 0.6928... Generator Loss: 1.4786
Epoch 1/1... Discriminator Loss: 0.8069... Generator Loss: 1.1422
Epoch 1/1... Discriminator Loss: 1.5382... Generator Loss: 0.7602
Epoch 1/1... Discriminator Loss: 1.0954... Generator Loss: 1.4506
Epoch 1/1... Discriminator Loss: 1.0280... Generator Loss: 1.5319
Epoch 1/1... Discriminator Loss: 1.2894... Generator Loss: 0.9598
Epoch 1/1... Discriminator Loss: 1.0394... Generator Loss: 1.1899
Epoch 1/1... Discriminator Loss: 1.7883... Generator Loss: 0.5698
Epoch 1/1... Discriminator Loss: 0.9225... Generator Loss: 1.0599
Epoch 1/1... Discriminator Loss: 1.3530... Generator Loss: 1.2169
Epoch 1/1... Discriminator Loss: 1.6620... Generator Loss: 0.7010
Epoch 1/1... Discriminator Loss: 0.7546... Generator Loss: 1.2308
Epoch 1/1... Discriminator Loss: 1.3719... Generator Loss: 0.7281
Epoch 1/1... Discriminator Loss: 0.8922... Generator Loss: 1.1641
Epoch 1/1... Discriminator Loss: 0.9991... Generator Loss: 0.8462
Epoch 1/1... Discriminator Loss: 1.1711... Generator Loss: 0.8294
Epoch 1/1... Discriminator Loss: 1.0254... Generator Loss: 1.3090
Epoch 1/1... Discriminator Loss: 1.0059... Generator Loss: 1.2162
Epoch 1/1... Discriminator Loss: 1.1595... Generator Loss: 0.8378
Epoch 1/1... Discriminator Loss: 0.9832... Generator Loss: 1.1170
Epoch 1/1... Discriminator Loss: 1.0280... Generator Loss: 2.8066
Epoch 1/1... Discriminator Loss: 1.0557... Generator Loss: 0.7329
Epoch 1/1... Discriminator Loss: 1.5968... Generator Loss: 0.5218
Epoch 1/1... Discriminator Loss: 1.2535... Generator Loss: 0.9759
Epoch 1/1... Discriminator Loss: 1.1413... Generator Loss: 0.7851
Epoch 1/1... Discriminator Loss: 1.2829... Generator Loss: 0.9619
Epoch 1/1... Discriminator Loss: 1.1898... Generator Loss: 0.6846
Epoch 1/1... Discriminator Loss: 1.2500... Generator Loss: 1.6583
Epoch 1/1... Discriminator Loss: 0.9917... Generator Loss: 0.9850
Epoch 1/1... Discriminator Loss: 1.3285... Generator Loss: 0.5654
Epoch 1/1... Discriminator Loss: 1.1351... Generator Loss: 0.9504
Epoch 1/1... Discriminator Loss: 1.4036... Generator Loss: 0.8903
Epoch 1/1... Discriminator Loss: 1.5470... Generator Loss: 0.6207
Epoch 1/1... Discriminator Loss: 1.1240... Generator Loss: 1.0057
Epoch 1/1... Discriminator Loss: 1.1442... Generator Loss: 1.4286
Epoch 1/1... Discriminator Loss: 1.2432... Generator Loss: 0.9627
Epoch 1/1... Discriminator Loss: 1.5292... Generator Loss: 2.2715
Epoch 1/1... Discriminator Loss: 0.8570... Generator Loss: 1.6316
Epoch 1/1... Discriminator Loss: 1.1823... Generator Loss: 0.7940
Epoch 1/1... Discriminator Loss: 0.9110... Generator Loss: 0.7905
Epoch 1/1... Discriminator Loss: 0.9785... Generator Loss: 0.9408
Epoch 1/1... Discriminator Loss: 1.5597... Generator Loss: 0.4518
Epoch 1/1... Discriminator Loss: 1.6631... Generator Loss: 0.6504
Epoch 1/1... Discriminator Loss: 0.9154... Generator Loss: 1.1072
Epoch 1/1... Discriminator Loss: 1.2410... Generator Loss: 1.0104
Epoch 1/1... Discriminator Loss: 1.2800... Generator Loss: 0.8525
Epoch 1/1... Discriminator Loss: 0.8570... Generator Loss: 1.1988
Epoch 1/1... Discriminator Loss: 1.4955... Generator Loss: 0.4049
Epoch 1/1... Discriminator Loss: 0.8449... Generator Loss: 1.1213
Epoch 1/1... Discriminator Loss: 1.3706... Generator Loss: 1.6853
Epoch 1/1... Discriminator Loss: 1.2753... Generator Loss: 0.7625
Epoch 1/1... Discriminator Loss: 0.7725... Generator Loss: 1.9453
Epoch 1/1... Discriminator Loss: 1.5660... Generator Loss: 0.6557
Epoch 1/1... Discriminator Loss: 0.8519... Generator Loss: 1.0239
Epoch 1/1... Discriminator Loss: 0.8004... Generator Loss: 1.2876
Epoch 1/1... Discriminator Loss: 0.9071... Generator Loss: 1.1757
Epoch 1/1... Discriminator Loss: 1.0826... Generator Loss: 1.2971
Epoch 1/1... Discriminator Loss: 0.7124... Generator Loss: 1.4085
Epoch 1/1... Discriminator Loss: 1.1038... Generator Loss: 0.9626
Epoch 1/1... Discriminator Loss: 1.0660... Generator Loss: 0.9672
Epoch 1/1... Discriminator Loss: 1.1199... Generator Loss: 1.1605
Epoch 1/1... Discriminator Loss: 1.4235... Generator Loss: 0.6259
Epoch 1/1... Discriminator Loss: 0.7696... Generator Loss: 1.7218
Epoch 1/1... Discriminator Loss: 1.3161... Generator Loss: 0.8547
Epoch 1/1... Discriminator Loss: 1.1983... Generator Loss: 0.7331
Epoch 1/1... Discriminator Loss: 0.9108... Generator Loss: 1.3288
Epoch 1/1... Discriminator Loss: 0.6913... Generator Loss: 1.6947
Epoch 1/1... Discriminator Loss: 1.1579... Generator Loss: 1.2266
Epoch 1/1... Discriminator Loss: 1.0321... Generator Loss: 1.0689
Epoch 1/1... Discriminator Loss: 0.9596... Generator Loss: 1.3092
Epoch 1/1... Discriminator Loss: 0.7994... Generator Loss: 1.2150
Epoch 1/1... Discriminator Loss: 0.7292... Generator Loss: 1.7562
Epoch 1/1... Discriminator Loss: 1.5081... Generator Loss: 0.4810
Epoch 1/1... Discriminator Loss: 1.1657... Generator Loss: 1.1103
Epoch 1/1... Discriminator Loss: 0.8869... Generator Loss: 1.2537
Epoch 1/1... Discriminator Loss: 1.0178... Generator Loss: 1.2846
Epoch 1/1... Discriminator Loss: 1.4579... Generator Loss: 0.6893
Epoch 1/1... Discriminator Loss: 0.8357... Generator Loss: 1.2567
Epoch 1/1... Discriminator Loss: 1.0443... Generator Loss: 1.3671
Epoch 1/1... Discriminator Loss: 0.8821... Generator Loss: 1.1209
Epoch 1/1... Discriminator Loss: 1.2454... Generator Loss: 1.6206
Epoch 1/1... Discriminator Loss: 1.4236... Generator Loss: 0.4608
Epoch 1/1... Discriminator Loss: 1.2520... Generator Loss: 0.9666
Epoch 1/1... Discriminator Loss: 0.7769... Generator Loss: 1.3125
Epoch 1/1... Discriminator Loss: 1.4243... Generator Loss: 0.9620
Epoch 1/1... Discriminator Loss: 1.0557... Generator Loss: 0.8916
Epoch 1/1... Discriminator Loss: 1.0411... Generator Loss: 1.5461
Epoch 1/1... Discriminator Loss: 0.9458... Generator Loss: 1.9129
Epoch 1/1... Discriminator Loss: 1.3450... Generator Loss: 0.6135
Epoch 1/1... Discriminator Loss: 0.9060... Generator Loss: 1.3808
Epoch 1/1... Discriminator Loss: 0.9760... Generator Loss: 1.1631
Epoch 1/1... Discriminator Loss: 1.1176... Generator Loss: 0.7761
Epoch 1/1... Discriminator Loss: 1.0257... Generator Loss: 1.1309
Epoch 1/1... Discriminator Loss: 1.3094... Generator Loss: 0.9832
Epoch 1/1... Discriminator Loss: 0.9404... Generator Loss: 1.1881
Epoch 1/1... Discriminator Loss: 1.1272... Generator Loss: 0.8722
Epoch 1/1... Discriminator Loss: 1.4861... Generator Loss: 0.8212
Epoch 1/1... Discriminator Loss: 1.0946... Generator Loss: 0.7148
Epoch 1/1... Discriminator Loss: 0.9243... Generator Loss: 1.5028
Epoch 1/1... Discriminator Loss: 1.0922... Generator Loss: 1.5595
Epoch 1/1... Discriminator Loss: 1.0648... Generator Loss: 1.1515
Epoch 1/1... Discriminator Loss: 1.3294... Generator Loss: 0.7444
Epoch 1/1... Discriminator Loss: 1.0287... Generator Loss: 0.8678
Epoch 1/1... Discriminator Loss: 0.6803... Generator Loss: 1.9289
Epoch 1/1... Discriminator Loss: 1.2050... Generator Loss: 0.6676
Epoch 1/1... Discriminator Loss: 0.7611... Generator Loss: 1.4789
Epoch 1/1... Discriminator Loss: 0.9220... Generator Loss: 1.2971

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

In [14]:
# this is to test with more epochs

batch_size = 64  #128  #64  #16  #64  #20  #5 
z_dim = 100  #128 # 200 #255
learning_rate = .0008  #.001  #.0001
beta1 = .7  #.45 #.6 #.8 #.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 4

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/4... Discriminator Loss: 0.4091... Generator Loss: 10.4503
Epoch 1/4... Discriminator Loss: 0.2579... Generator Loss: 2.6991
Epoch 1/4... Discriminator Loss: 0.1897... Generator Loss: 4.2393
Epoch 1/4... Discriminator Loss: 0.1929... Generator Loss: 4.4412
Epoch 1/4... Discriminator Loss: 0.1081... Generator Loss: 3.8239
Epoch 1/4... Discriminator Loss: 0.1146... Generator Loss: 5.5632
Epoch 1/4... Discriminator Loss: 0.0990... Generator Loss: 4.8105
Epoch 1/4... Discriminator Loss: 0.3777... Generator Loss: 4.8724
Epoch 1/4... Discriminator Loss: 0.2026... Generator Loss: 3.5615
Epoch 1/4... Discriminator Loss: 0.5111... Generator Loss: 2.6247
Epoch 1/4... Discriminator Loss: 1.7179... Generator Loss: 5.8523
Epoch 1/4... Discriminator Loss: 1.4177... Generator Loss: 4.2990
Epoch 1/4... Discriminator Loss: 0.4835... Generator Loss: 2.9266
Epoch 1/4... Discriminator Loss: 1.0110... Generator Loss: 4.0838
Epoch 1/4... Discriminator Loss: 1.7081... Generator Loss: 0.4798
Epoch 1/4... Discriminator Loss: 0.7998... Generator Loss: 1.2311
Epoch 1/4... Discriminator Loss: 0.8138... Generator Loss: 2.1864
Epoch 1/4... Discriminator Loss: 1.0202... Generator Loss: 1.0077
Epoch 1/4... Discriminator Loss: 0.5364... Generator Loss: 1.5687
Epoch 1/4... Discriminator Loss: 1.1605... Generator Loss: 0.7526
Epoch 1/4... Discriminator Loss: 1.3262... Generator Loss: 0.7371
Epoch 1/4... Discriminator Loss: 1.4150... Generator Loss: 0.5195
Epoch 1/4... Discriminator Loss: 1.0232... Generator Loss: 1.1479
Epoch 1/4... Discriminator Loss: 1.1263... Generator Loss: 1.7536
Epoch 1/4... Discriminator Loss: 0.9851... Generator Loss: 0.7632
Epoch 1/4... Discriminator Loss: 1.0399... Generator Loss: 0.8404
Epoch 1/4... Discriminator Loss: 1.1185... Generator Loss: 1.3265
Epoch 1/4... Discriminator Loss: 1.0843... Generator Loss: 1.0208
Epoch 1/4... Discriminator Loss: 1.4723... Generator Loss: 0.7860
Epoch 1/4... Discriminator Loss: 2.5076... Generator Loss: 0.1306
Epoch 1/4... Discriminator Loss: 0.8424... Generator Loss: 1.3912
Epoch 1/4... Discriminator Loss: 1.4180... Generator Loss: 1.3965
Epoch 1/4... Discriminator Loss: 1.0941... Generator Loss: 0.9579
Epoch 1/4... Discriminator Loss: 0.9632... Generator Loss: 2.0155
Epoch 1/4... Discriminator Loss: 1.0167... Generator Loss: 1.0001
Epoch 1/4... Discriminator Loss: 1.1532... Generator Loss: 1.5980
Epoch 1/4... Discriminator Loss: 1.2766... Generator Loss: 1.2278
Epoch 1/4... Discriminator Loss: 1.1421... Generator Loss: 1.9286
Epoch 1/4... Discriminator Loss: 0.9202... Generator Loss: 0.9444
Epoch 1/4... Discriminator Loss: 1.3173... Generator Loss: 1.7063
Epoch 1/4... Discriminator Loss: 0.8950... Generator Loss: 1.7086
Epoch 1/4... Discriminator Loss: 1.1033... Generator Loss: 1.3767
Epoch 1/4... Discriminator Loss: 1.2346... Generator Loss: 0.7017
Epoch 1/4... Discriminator Loss: 0.7215... Generator Loss: 1.4088
Epoch 1/4... Discriminator Loss: 1.1400... Generator Loss: 0.8093
Epoch 1/4... Discriminator Loss: 1.2039... Generator Loss: 0.7500
Epoch 1/4... Discriminator Loss: 1.2326... Generator Loss: 0.8841
Epoch 1/4... Discriminator Loss: 0.7555... Generator Loss: 1.2598
Epoch 1/4... Discriminator Loss: 1.3178... Generator Loss: 0.6254
Epoch 1/4... Discriminator Loss: 1.1622... Generator Loss: 0.7728
Epoch 1/4... Discriminator Loss: 1.5206... Generator Loss: 0.5796
Epoch 1/4... Discriminator Loss: 1.5254... Generator Loss: 1.1810
Epoch 1/4... Discriminator Loss: 1.5503... Generator Loss: 0.4685
Epoch 1/4... Discriminator Loss: 0.8037... Generator Loss: 1.1417
Epoch 1/4... Discriminator Loss: 1.1151... Generator Loss: 0.9527
Epoch 1/4... Discriminator Loss: 0.9862... Generator Loss: 0.7591
Epoch 1/4... Discriminator Loss: 1.1014... Generator Loss: 1.0897
Epoch 1/4... Discriminator Loss: 1.4845... Generator Loss: 1.3807
Epoch 1/4... Discriminator Loss: 0.9706... Generator Loss: 1.3442
Epoch 1/4... Discriminator Loss: 1.1538... Generator Loss: 0.5770
Epoch 1/4... Discriminator Loss: 1.4195... Generator Loss: 0.4827
Epoch 1/4... Discriminator Loss: 1.2508... Generator Loss: 0.7061
Epoch 1/4... Discriminator Loss: 0.6581... Generator Loss: 1.4664
Epoch 1/4... Discriminator Loss: 0.9030... Generator Loss: 0.7934
Epoch 1/4... Discriminator Loss: 1.0737... Generator Loss: 1.1734
Epoch 1/4... Discriminator Loss: 0.8258... Generator Loss: 1.1225
Epoch 1/4... Discriminator Loss: 0.9172... Generator Loss: 1.0979
Epoch 1/4... Discriminator Loss: 0.9609... Generator Loss: 0.9681
Epoch 1/4... Discriminator Loss: 1.1599... Generator Loss: 0.9034
Epoch 1/4... Discriminator Loss: 1.2066... Generator Loss: 1.3621
Epoch 1/4... Discriminator Loss: 1.0337... Generator Loss: 0.8816
Epoch 1/4... Discriminator Loss: 1.3176... Generator Loss: 0.5291
Epoch 1/4... Discriminator Loss: 0.9452... Generator Loss: 1.3865
Epoch 1/4... Discriminator Loss: 1.1108... Generator Loss: 0.8776
Epoch 1/4... Discriminator Loss: 1.1451... Generator Loss: 1.9103
Epoch 1/4... Discriminator Loss: 0.9882... Generator Loss: 1.3728
Epoch 1/4... Discriminator Loss: 0.8213... Generator Loss: 1.6016
Epoch 1/4... Discriminator Loss: 1.0951... Generator Loss: 0.8219
Epoch 1/4... Discriminator Loss: 1.0319... Generator Loss: 1.1439
Epoch 1/4... Discriminator Loss: 1.2364... Generator Loss: 1.6625
Epoch 1/4... Discriminator Loss: 1.0773... Generator Loss: 1.0091
Epoch 1/4... Discriminator Loss: 1.5533... Generator Loss: 2.0082
Epoch 1/4... Discriminator Loss: 1.3950... Generator Loss: 0.7797
Epoch 1/4... Discriminator Loss: 1.4663... Generator Loss: 0.9674
Epoch 1/4... Discriminator Loss: 0.8763... Generator Loss: 1.0591
Epoch 1/4... Discriminator Loss: 0.9358... Generator Loss: 1.0279
Epoch 1/4... Discriminator Loss: 1.1151... Generator Loss: 1.1191
Epoch 1/4... Discriminator Loss: 1.1822... Generator Loss: 0.8974
Epoch 1/4... Discriminator Loss: 1.1081... Generator Loss: 0.8045
Epoch 1/4... Discriminator Loss: 1.0650... Generator Loss: 0.9226
Epoch 1/4... Discriminator Loss: 1.2012... Generator Loss: 0.7947
Epoch 1/4... Discriminator Loss: 1.3178... Generator Loss: 0.6757
Epoch 1/4... Discriminator Loss: 0.9531... Generator Loss: 1.1923
Epoch 1/4... Discriminator Loss: 0.9495... Generator Loss: 1.0575
Epoch 1/4... Discriminator Loss: 0.9043... Generator Loss: 1.1727
Epoch 1/4... Discriminator Loss: 0.9416... Generator Loss: 0.7760
Epoch 1/4... Discriminator Loss: 1.1774... Generator Loss: 0.6482
Epoch 1/4... Discriminator Loss: 1.0999... Generator Loss: 0.9973
Epoch 1/4... Discriminator Loss: 0.9110... Generator Loss: 1.0002
Epoch 1/4... Discriminator Loss: 1.3145... Generator Loss: 1.3048
Epoch 1/4... Discriminator Loss: 0.9418... Generator Loss: 1.3953
Epoch 1/4... Discriminator Loss: 0.9041... Generator Loss: 1.2717
Epoch 1/4... Discriminator Loss: 1.3051... Generator Loss: 1.0506
Epoch 1/4... Discriminator Loss: 1.3584... Generator Loss: 0.9509
Epoch 1/4... Discriminator Loss: 1.0278... Generator Loss: 1.2281
Epoch 1/4... Discriminator Loss: 0.9995... Generator Loss: 1.3705
Epoch 1/4... Discriminator Loss: 1.0126... Generator Loss: 2.2201
Epoch 1/4... Discriminator Loss: 1.2714... Generator Loss: 1.0443
Epoch 1/4... Discriminator Loss: 0.8674... Generator Loss: 1.0279
Epoch 1/4... Discriminator Loss: 1.1531... Generator Loss: 1.8856
Epoch 1/4... Discriminator Loss: 1.0305... Generator Loss: 1.1975
Epoch 1/4... Discriminator Loss: 1.1066... Generator Loss: 1.1360
Epoch 1/4... Discriminator Loss: 0.9016... Generator Loss: 0.9322
Epoch 1/4... Discriminator Loss: 1.0075... Generator Loss: 0.7416
Epoch 1/4... Discriminator Loss: 1.1676... Generator Loss: 1.1992
Epoch 1/4... Discriminator Loss: 0.9507... Generator Loss: 1.2960
Epoch 1/4... Discriminator Loss: 1.4638... Generator Loss: 0.5760
Epoch 1/4... Discriminator Loss: 0.7459... Generator Loss: 1.2065
Epoch 1/4... Discriminator Loss: 1.0372... Generator Loss: 1.2656
Epoch 1/4... Discriminator Loss: 0.9554... Generator Loss: 0.9325
Epoch 1/4... Discriminator Loss: 1.0741... Generator Loss: 0.7993
Epoch 1/4... Discriminator Loss: 1.0975... Generator Loss: 1.5315
Epoch 1/4... Discriminator Loss: 1.4086... Generator Loss: 0.7636
Epoch 1/4... Discriminator Loss: 1.4599... Generator Loss: 0.3727
Epoch 1/4... Discriminator Loss: 0.6583... Generator Loss: 1.4891
Epoch 1/4... Discriminator Loss: 1.0232... Generator Loss: 1.1089
Epoch 2/4... Discriminator Loss: 0.8850... Generator Loss: 1.6461
Epoch 2/4... Discriminator Loss: 0.9592... Generator Loss: 1.1210
Epoch 2/4... Discriminator Loss: 0.6432... Generator Loss: 1.6563
Epoch 2/4... Discriminator Loss: 1.1638... Generator Loss: 1.3033
Epoch 2/4... Discriminator Loss: 0.8185... Generator Loss: 1.2263
Epoch 2/4... Discriminator Loss: 1.1222... Generator Loss: 0.6613
Epoch 2/4... Discriminator Loss: 0.8718... Generator Loss: 1.1891
Epoch 2/4... Discriminator Loss: 0.8063... Generator Loss: 1.2227
Epoch 2/4... Discriminator Loss: 0.9161... Generator Loss: 1.8887
Epoch 2/4... Discriminator Loss: 0.9055... Generator Loss: 0.9441
Epoch 2/4... Discriminator Loss: 0.6807... Generator Loss: 1.4741
Epoch 2/4... Discriminator Loss: 1.1733... Generator Loss: 1.0370
Epoch 2/4... Discriminator Loss: 0.7530... Generator Loss: 1.4841
Epoch 2/4... Discriminator Loss: 1.0012... Generator Loss: 1.7220
Epoch 2/4... Discriminator Loss: 0.9062... Generator Loss: 1.1823
Epoch 2/4... Discriminator Loss: 0.9953... Generator Loss: 1.0741
Epoch 2/4... Discriminator Loss: 0.6830... Generator Loss: 1.8390
Epoch 2/4... Discriminator Loss: 1.0876... Generator Loss: 1.5394
Epoch 2/4... Discriminator Loss: 0.8379... Generator Loss: 1.4753
Epoch 2/4... Discriminator Loss: 0.6521... Generator Loss: 1.4250
Epoch 2/4... Discriminator Loss: 1.0362... Generator Loss: 1.0228
Epoch 2/4... Discriminator Loss: 0.9247... Generator Loss: 1.0033
Epoch 2/4... Discriminator Loss: 1.2313... Generator Loss: 0.6507
Epoch 2/4... Discriminator Loss: 0.6169... Generator Loss: 1.7436
Epoch 2/4... Discriminator Loss: 1.0063... Generator Loss: 1.0675
Epoch 2/4... Discriminator Loss: 0.8732... Generator Loss: 1.2724
Epoch 2/4... Discriminator Loss: 1.0469... Generator Loss: 0.8471
Epoch 2/4... Discriminator Loss: 0.9050... Generator Loss: 1.6022
Epoch 2/4... Discriminator Loss: 0.7918... Generator Loss: 1.6078
Epoch 2/4... Discriminator Loss: 0.9000... Generator Loss: 1.1933
Epoch 2/4... Discriminator Loss: 0.8452... Generator Loss: 1.2257
Epoch 2/4... Discriminator Loss: 1.0710... Generator Loss: 1.0577
Epoch 2/4... Discriminator Loss: 0.6400... Generator Loss: 1.3751
Epoch 2/4... Discriminator Loss: 1.0744... Generator Loss: 0.7320
Epoch 2/4... Discriminator Loss: 1.0226... Generator Loss: 1.0539
Epoch 2/4... Discriminator Loss: 1.0091... Generator Loss: 0.7958
Epoch 2/4... Discriminator Loss: 0.8873... Generator Loss: 1.1665
Epoch 2/4... Discriminator Loss: 0.7454... Generator Loss: 1.3159
Epoch 2/4... Discriminator Loss: 1.0426... Generator Loss: 1.0082
Epoch 2/4... Discriminator Loss: 1.0609... Generator Loss: 0.9878
Epoch 2/4... Discriminator Loss: 1.0105... Generator Loss: 1.4370
Epoch 2/4... Discriminator Loss: 1.5699... Generator Loss: 0.8723
Epoch 2/4... Discriminator Loss: 1.0294... Generator Loss: 1.4694
Epoch 2/4... Discriminator Loss: 0.7234... Generator Loss: 1.3754
Epoch 2/4... Discriminator Loss: 0.7935... Generator Loss: 1.6701
Epoch 2/4... Discriminator Loss: 0.8051... Generator Loss: 1.2989
Epoch 2/4... Discriminator Loss: 1.1244... Generator Loss: 0.9019
Epoch 2/4... Discriminator Loss: 0.8580... Generator Loss: 1.0986
Epoch 2/4... Discriminator Loss: 0.8214... Generator Loss: 2.1789
Epoch 2/4... Discriminator Loss: 0.5787... Generator Loss: 1.7808
Epoch 2/4... Discriminator Loss: 0.8713... Generator Loss: 1.2015
Epoch 2/4... Discriminator Loss: 0.6396... Generator Loss: 1.2416
Epoch 2/4... Discriminator Loss: 1.3637... Generator Loss: 1.2079
Epoch 2/4... Discriminator Loss: 0.6000... Generator Loss: 1.7397
Epoch 2/4... Discriminator Loss: 0.8934... Generator Loss: 1.2491
Epoch 2/4... Discriminator Loss: 0.8109... Generator Loss: 1.0645
Epoch 2/4... Discriminator Loss: 0.6013... Generator Loss: 1.8522
Epoch 2/4... Discriminator Loss: 0.9583... Generator Loss: 1.4777
Epoch 2/4... Discriminator Loss: 0.6891... Generator Loss: 1.3020
Epoch 2/4... Discriminator Loss: 0.5446... Generator Loss: 2.2758
Epoch 2/4... Discriminator Loss: 0.9013... Generator Loss: 1.0328
Epoch 2/4... Discriminator Loss: 1.0479... Generator Loss: 1.9543
Epoch 2/4... Discriminator Loss: 1.0565... Generator Loss: 0.8199
Epoch 2/4... Discriminator Loss: 0.9805... Generator Loss: 0.9024
Epoch 2/4... Discriminator Loss: 0.6537... Generator Loss: 1.4470
Epoch 2/4... Discriminator Loss: 0.9977... Generator Loss: 1.3306
Epoch 2/4... Discriminator Loss: 0.5718... Generator Loss: 2.4635
Epoch 2/4... Discriminator Loss: 1.0966... Generator Loss: 0.7196
Epoch 2/4... Discriminator Loss: 1.0767... Generator Loss: 1.1449
Epoch 2/4... Discriminator Loss: 0.7733... Generator Loss: 1.5066
Epoch 2/4... Discriminator Loss: 0.9144... Generator Loss: 0.8657
Epoch 2/4... Discriminator Loss: 0.8465... Generator Loss: 1.1769
Epoch 2/4... Discriminator Loss: 0.8378... Generator Loss: 1.4525
Epoch 2/4... Discriminator Loss: 0.7404... Generator Loss: 1.2844
Epoch 2/4... Discriminator Loss: 0.8178... Generator Loss: 1.4618
Epoch 2/4... Discriminator Loss: 0.9564... Generator Loss: 1.3762
Epoch 2/4... Discriminator Loss: 0.9662... Generator Loss: 1.8414
Epoch 2/4... Discriminator Loss: 1.2636... Generator Loss: 0.5592
Epoch 2/4... Discriminator Loss: 0.8265... Generator Loss: 1.0608
Epoch 2/4... Discriminator Loss: 0.9858... Generator Loss: 1.3166
Epoch 2/4... Discriminator Loss: 0.7449... Generator Loss: 1.6671
Epoch 2/4... Discriminator Loss: 0.7330... Generator Loss: 2.0728
Epoch 2/4... Discriminator Loss: 0.6835... Generator Loss: 2.1691
Epoch 2/4... Discriminator Loss: 0.9093... Generator Loss: 1.8212
Epoch 2/4... Discriminator Loss: 0.8976... Generator Loss: 0.9015
Epoch 2/4... Discriminator Loss: 1.0192... Generator Loss: 0.9552
Epoch 2/4... Discriminator Loss: 0.7755... Generator Loss: 1.9214
Epoch 2/4... Discriminator Loss: 1.2865... Generator Loss: 0.5452
Epoch 2/4... Discriminator Loss: 1.0767... Generator Loss: 0.8223
Epoch 2/4... Discriminator Loss: 0.7166... Generator Loss: 1.6499
Epoch 2/4... Discriminator Loss: 0.8878... Generator Loss: 1.2556
Epoch 2/4... Discriminator Loss: 0.8712... Generator Loss: 1.8811
Epoch 2/4... Discriminator Loss: 0.6391... Generator Loss: 1.5440
Epoch 2/4... Discriminator Loss: 0.8558... Generator Loss: 1.7891
Epoch 2/4... Discriminator Loss: 0.7200... Generator Loss: 1.3450
Epoch 2/4... Discriminator Loss: 0.8398... Generator Loss: 0.9063
Epoch 2/4... Discriminator Loss: 0.8455... Generator Loss: 1.3596
Epoch 2/4... Discriminator Loss: 0.8559... Generator Loss: 0.8362
Epoch 2/4... Discriminator Loss: 0.9575... Generator Loss: 1.1107
Epoch 2/4... Discriminator Loss: 0.6913... Generator Loss: 1.8150
Epoch 2/4... Discriminator Loss: 0.7421... Generator Loss: 1.3436
Epoch 2/4... Discriminator Loss: 0.5741... Generator Loss: 1.2814
Epoch 2/4... Discriminator Loss: 0.8828... Generator Loss: 1.0207
Epoch 2/4... Discriminator Loss: 0.6144... Generator Loss: 1.9245
Epoch 2/4... Discriminator Loss: 0.9725... Generator Loss: 1.0274
Epoch 2/4... Discriminator Loss: 0.5178... Generator Loss: 1.5692
Epoch 2/4... Discriminator Loss: 0.5171... Generator Loss: 1.6617
Epoch 2/4... Discriminator Loss: 1.0879... Generator Loss: 0.7267
Epoch 2/4... Discriminator Loss: 0.6295... Generator Loss: 1.8277
Epoch 2/4... Discriminator Loss: 1.0433... Generator Loss: 0.8544
Epoch 2/4... Discriminator Loss: 0.8505... Generator Loss: 1.0101
Epoch 2/4... Discriminator Loss: 0.7075... Generator Loss: 1.1191
Epoch 2/4... Discriminator Loss: 0.6606... Generator Loss: 1.9059
Epoch 2/4... Discriminator Loss: 0.6051... Generator Loss: 1.6056
Epoch 2/4... Discriminator Loss: 0.5610... Generator Loss: 1.9110
Epoch 2/4... Discriminator Loss: 1.3741... Generator Loss: 0.4661
Epoch 2/4... Discriminator Loss: 0.8278... Generator Loss: 1.0864
Epoch 2/4... Discriminator Loss: 0.6746... Generator Loss: 1.8826
Epoch 2/4... Discriminator Loss: 0.6196... Generator Loss: 1.3767
Epoch 2/4... Discriminator Loss: 0.5743... Generator Loss: 1.7190
Epoch 2/4... Discriminator Loss: 1.2672... Generator Loss: 1.3507
Epoch 2/4... Discriminator Loss: 0.6468... Generator Loss: 1.3806
Epoch 2/4... Discriminator Loss: 0.7742... Generator Loss: 1.0668
Epoch 2/4... Discriminator Loss: 0.8284... Generator Loss: 1.3434
Epoch 2/4... Discriminator Loss: 0.6118... Generator Loss: 1.4007
Epoch 2/4... Discriminator Loss: 0.5969... Generator Loss: 1.7731
Epoch 2/4... Discriminator Loss: 0.8589... Generator Loss: 2.2448
Epoch 3/4... Discriminator Loss: 1.1839... Generator Loss: 0.6200
Epoch 3/4... Discriminator Loss: 0.5771... Generator Loss: 1.4138
Epoch 3/4... Discriminator Loss: 0.8773... Generator Loss: 1.2105
Epoch 3/4... Discriminator Loss: 0.9003... Generator Loss: 1.1831
Epoch 3/4... Discriminator Loss: 0.9870... Generator Loss: 0.9342
Epoch 3/4... Discriminator Loss: 1.0597... Generator Loss: 0.7951
Epoch 3/4... Discriminator Loss: 0.6701... Generator Loss: 1.5888
Epoch 3/4... Discriminator Loss: 0.6792... Generator Loss: 1.5508
Epoch 3/4... Discriminator Loss: 1.0129... Generator Loss: 1.5297
Epoch 3/4... Discriminator Loss: 0.8550... Generator Loss: 1.6224
Epoch 3/4... Discriminator Loss: 0.9267... Generator Loss: 0.8762
Epoch 3/4... Discriminator Loss: 0.7958... Generator Loss: 1.4237
Epoch 3/4... Discriminator Loss: 0.8675... Generator Loss: 1.2690
Epoch 3/4... Discriminator Loss: 1.0380... Generator Loss: 0.8521
Epoch 3/4... Discriminator Loss: 0.5679... Generator Loss: 1.5510
Epoch 3/4... Discriminator Loss: 0.6484... Generator Loss: 1.2825
Epoch 3/4... Discriminator Loss: 0.5525... Generator Loss: 1.7356
Epoch 3/4... Discriminator Loss: 0.6859... Generator Loss: 2.1149
Epoch 3/4... Discriminator Loss: 0.9208... Generator Loss: 0.9354
Epoch 3/4... Discriminator Loss: 0.7430... Generator Loss: 1.6130
Epoch 3/4... Discriminator Loss: 0.8331... Generator Loss: 1.1650
Epoch 3/4... Discriminator Loss: 0.9721... Generator Loss: 1.3130
Epoch 3/4... Discriminator Loss: 1.0342... Generator Loss: 0.8480
Epoch 3/4... Discriminator Loss: 0.6342... Generator Loss: 2.0624
Epoch 3/4... Discriminator Loss: 1.0033... Generator Loss: 1.0602
Epoch 3/4... Discriminator Loss: 0.7677... Generator Loss: 1.4956
Epoch 3/4... Discriminator Loss: 0.8122... Generator Loss: 0.9993
Epoch 3/4... Discriminator Loss: 0.6500... Generator Loss: 1.4771
Epoch 3/4... Discriminator Loss: 0.7619... Generator Loss: 1.3463
Epoch 3/4... Discriminator Loss: 0.6486... Generator Loss: 1.6556
Epoch 3/4... Discriminator Loss: 0.8089... Generator Loss: 1.1881
Epoch 3/4... Discriminator Loss: 0.8116... Generator Loss: 1.6825
Epoch 3/4... Discriminator Loss: 0.6007... Generator Loss: 1.6090
Epoch 3/4... Discriminator Loss: 0.8028... Generator Loss: 1.5192
Epoch 3/4... Discriminator Loss: 0.7380... Generator Loss: 1.2976
Epoch 3/4... Discriminator Loss: 0.8060... Generator Loss: 1.0862
Epoch 3/4... Discriminator Loss: 0.6953... Generator Loss: 1.2726
Epoch 3/4... Discriminator Loss: 0.7382... Generator Loss: 1.3288
Epoch 3/4... Discriminator Loss: 0.7497... Generator Loss: 2.2412
Epoch 3/4... Discriminator Loss: 0.7770... Generator Loss: 1.1143
Epoch 3/4... Discriminator Loss: 0.5966... Generator Loss: 1.3742
Epoch 3/4... Discriminator Loss: 0.7685... Generator Loss: 1.4076
Epoch 3/4... Discriminator Loss: 0.6815... Generator Loss: 1.3433
Epoch 3/4... Discriminator Loss: 0.6670... Generator Loss: 1.4656
Epoch 3/4... Discriminator Loss: 0.9262... Generator Loss: 0.7879
Epoch 3/4... Discriminator Loss: 0.8937... Generator Loss: 1.3137
Epoch 3/4... Discriminator Loss: 0.9253... Generator Loss: 0.9454
Epoch 3/4... Discriminator Loss: 0.9225... Generator Loss: 1.8703
Epoch 3/4... Discriminator Loss: 0.8550... Generator Loss: 1.1097
Epoch 3/4... Discriminator Loss: 1.0147... Generator Loss: 0.8855
Epoch 3/4... Discriminator Loss: 1.0001... Generator Loss: 0.8786
Epoch 3/4... Discriminator Loss: 0.9155... Generator Loss: 1.6221
Epoch 3/4... Discriminator Loss: 1.0428... Generator Loss: 0.7939
Epoch 3/4... Discriminator Loss: 0.8823... Generator Loss: 0.9932
Epoch 3/4... Discriminator Loss: 0.6274... Generator Loss: 1.5978
Epoch 3/4... Discriminator Loss: 0.9046... Generator Loss: 0.9258
Epoch 3/4... Discriminator Loss: 0.6976... Generator Loss: 1.3427
Epoch 3/4... Discriminator Loss: 0.5369... Generator Loss: 1.5254
Epoch 3/4... Discriminator Loss: 0.9353... Generator Loss: 0.9868
Epoch 3/4... Discriminator Loss: 0.9289... Generator Loss: 0.8978
Epoch 3/4... Discriminator Loss: 0.6694... Generator Loss: 1.6586
Epoch 3/4... Discriminator Loss: 0.6146... Generator Loss: 1.3310
Epoch 3/4... Discriminator Loss: 0.7013... Generator Loss: 1.4908
Epoch 3/4... Discriminator Loss: 0.8533... Generator Loss: 1.7169
Epoch 3/4... Discriminator Loss: 0.5668... Generator Loss: 1.6587
Epoch 3/4... Discriminator Loss: 1.0456... Generator Loss: 0.8521
Epoch 3/4... Discriminator Loss: 0.3177... Generator Loss: 2.7258
Epoch 3/4... Discriminator Loss: 0.8911... Generator Loss: 1.0659
Epoch 3/4... Discriminator Loss: 0.8390... Generator Loss: 1.3708
Epoch 3/4... Discriminator Loss: 0.7426... Generator Loss: 2.3778
Epoch 3/4... Discriminator Loss: 0.7096... Generator Loss: 1.4909
Epoch 3/4... Discriminator Loss: 1.0074... Generator Loss: 0.8522
Epoch 3/4... Discriminator Loss: 0.7367... Generator Loss: 1.3224
Epoch 3/4... Discriminator Loss: 1.1630... Generator Loss: 0.6646
Epoch 3/4... Discriminator Loss: 0.6273... Generator Loss: 1.7477
Epoch 3/4... Discriminator Loss: 1.1647... Generator Loss: 1.4118
Epoch 3/4... Discriminator Loss: 0.7578... Generator Loss: 1.0638
Epoch 3/4... Discriminator Loss: 0.6374... Generator Loss: 2.2899
Epoch 3/4... Discriminator Loss: 0.6048... Generator Loss: 1.4966
Epoch 3/4... Discriminator Loss: 0.7657... Generator Loss: 1.7418
Epoch 3/4... Discriminator Loss: 0.7837... Generator Loss: 1.2400
Epoch 3/4... Discriminator Loss: 0.6409... Generator Loss: 1.4003
Epoch 3/4... Discriminator Loss: 0.7296... Generator Loss: 1.4203
Epoch 3/4... Discriminator Loss: 0.8981... Generator Loss: 1.2500
Epoch 3/4... Discriminator Loss: 0.6143... Generator Loss: 1.4821
Epoch 3/4... Discriminator Loss: 0.7530... Generator Loss: 1.1879
Epoch 3/4... Discriminator Loss: 0.7993... Generator Loss: 1.2329
Epoch 3/4... Discriminator Loss: 0.7974... Generator Loss: 2.4008
Epoch 3/4... Discriminator Loss: 0.8489... Generator Loss: 1.3404
Epoch 3/4... Discriminator Loss: 0.6989... Generator Loss: 1.8835
Epoch 3/4... Discriminator Loss: 1.1446... Generator Loss: 1.9128
Epoch 3/4... Discriminator Loss: 0.7533... Generator Loss: 1.9441
Epoch 3/4... Discriminator Loss: 0.7344... Generator Loss: 1.4308
Epoch 3/4... Discriminator Loss: 0.7983... Generator Loss: 2.1851
Epoch 3/4... Discriminator Loss: 0.8190... Generator Loss: 0.9650
Epoch 3/4... Discriminator Loss: 0.8108... Generator Loss: 1.3624
Epoch 3/4... Discriminator Loss: 0.7953... Generator Loss: 2.1320
Epoch 3/4... Discriminator Loss: 1.0892... Generator Loss: 0.7087
Epoch 3/4... Discriminator Loss: 1.0890... Generator Loss: 0.7146
Epoch 3/4... Discriminator Loss: 0.6982... Generator Loss: 1.2822
Epoch 3/4... Discriminator Loss: 0.4874... Generator Loss: 1.6222
Epoch 3/4... Discriminator Loss: 0.7053... Generator Loss: 1.3278
Epoch 3/4... Discriminator Loss: 1.0720... Generator Loss: 0.8146
Epoch 3/4... Discriminator Loss: 0.9733... Generator Loss: 1.0427
Epoch 3/4... Discriminator Loss: 0.9353... Generator Loss: 1.3223
Epoch 3/4... Discriminator Loss: 0.8736... Generator Loss: 1.0238
Epoch 3/4... Discriminator Loss: 0.5184... Generator Loss: 1.6509
Epoch 3/4... Discriminator Loss: 0.6504... Generator Loss: 1.7778
Epoch 3/4... Discriminator Loss: 0.5962... Generator Loss: 2.6066
Epoch 3/4... Discriminator Loss: 0.6757... Generator Loss: 1.8349
Epoch 3/4... Discriminator Loss: 0.5738... Generator Loss: 1.6200
Epoch 3/4... Discriminator Loss: 0.6916... Generator Loss: 1.2581
Epoch 3/4... Discriminator Loss: 0.7283... Generator Loss: 1.2804
Epoch 3/4... Discriminator Loss: 1.0544... Generator Loss: 0.7598
Epoch 3/4... Discriminator Loss: 0.7521... Generator Loss: 1.1287
Epoch 3/4... Discriminator Loss: 0.5540... Generator Loss: 2.1123
Epoch 3/4... Discriminator Loss: 0.6425... Generator Loss: 1.7379
Epoch 3/4... Discriminator Loss: 0.9004... Generator Loss: 0.8644
Epoch 3/4... Discriminator Loss: 0.7439... Generator Loss: 1.2437
Epoch 3/4... Discriminator Loss: 0.7961... Generator Loss: 2.2926
Epoch 3/4... Discriminator Loss: 0.6614... Generator Loss: 1.3469
Epoch 3/4... Discriminator Loss: 0.7937... Generator Loss: 1.2228
Epoch 3/4... Discriminator Loss: 0.6542... Generator Loss: 1.7103
Epoch 3/4... Discriminator Loss: 0.4247... Generator Loss: 2.3003
Epoch 3/4... Discriminator Loss: 0.4949... Generator Loss: 1.7981
Epoch 3/4... Discriminator Loss: 0.5763... Generator Loss: 1.5632
Epoch 4/4... Discriminator Loss: 0.8377... Generator Loss: 1.1149
Epoch 4/4... Discriminator Loss: 0.6472... Generator Loss: 1.6746
Epoch 4/4... Discriminator Loss: 0.7700... Generator Loss: 1.7512
Epoch 4/4... Discriminator Loss: 0.7786... Generator Loss: 1.2687
Epoch 4/4... Discriminator Loss: 0.7567... Generator Loss: 1.1108
Epoch 4/4... Discriminator Loss: 0.6514... Generator Loss: 1.3802
Epoch 4/4... Discriminator Loss: 0.4676... Generator Loss: 1.9431
Epoch 4/4... Discriminator Loss: 0.5965... Generator Loss: 1.8895
Epoch 4/4... Discriminator Loss: 0.6022... Generator Loss: 1.6003
Epoch 4/4... Discriminator Loss: 0.5732... Generator Loss: 1.6204
Epoch 4/4... Discriminator Loss: 0.7074... Generator Loss: 1.3832
Epoch 4/4... Discriminator Loss: 1.1469... Generator Loss: 0.7349
Epoch 4/4... Discriminator Loss: 0.4376... Generator Loss: 2.2658
Epoch 4/4... Discriminator Loss: 1.2622... Generator Loss: 0.9735
Epoch 4/4... Discriminator Loss: 0.5885... Generator Loss: 1.7053
Epoch 4/4... Discriminator Loss: 1.1581... Generator Loss: 0.7904
Epoch 4/4... Discriminator Loss: 0.6590... Generator Loss: 2.3030
Epoch 4/4... Discriminator Loss: 0.8641... Generator Loss: 1.4459
Epoch 4/4... Discriminator Loss: 0.9649... Generator Loss: 1.5642
Epoch 4/4... Discriminator Loss: 1.0095... Generator Loss: 0.9502
Epoch 4/4... Discriminator Loss: 1.0605... Generator Loss: 0.7891
Epoch 4/4... Discriminator Loss: 0.8606... Generator Loss: 1.3090
Epoch 4/4... Discriminator Loss: 0.9371... Generator Loss: 1.3897
Epoch 4/4... Discriminator Loss: 0.8353... Generator Loss: 1.1232
Epoch 4/4... Discriminator Loss: 0.5058... Generator Loss: 2.3309
Epoch 4/4... Discriminator Loss: 1.0840... Generator Loss: 0.9110
Epoch 4/4... Discriminator Loss: 0.8571... Generator Loss: 1.5053
Epoch 4/4... Discriminator Loss: 0.7864... Generator Loss: 1.1633
Epoch 4/4... Discriminator Loss: 0.7174... Generator Loss: 1.5845
Epoch 4/4... Discriminator Loss: 0.9728... Generator Loss: 0.9629
Epoch 4/4... Discriminator Loss: 0.7859... Generator Loss: 1.1769
Epoch 4/4... Discriminator Loss: 0.5958... Generator Loss: 2.1553
Epoch 4/4... Discriminator Loss: 0.6618... Generator Loss: 1.8840
Epoch 4/4... Discriminator Loss: 0.6552... Generator Loss: 1.3158
Epoch 4/4... Discriminator Loss: 0.9282... Generator Loss: 0.8879
Epoch 4/4... Discriminator Loss: 1.2857... Generator Loss: 0.6790
Epoch 4/4... Discriminator Loss: 0.5739... Generator Loss: 1.5032
Epoch 4/4... Discriminator Loss: 0.6444... Generator Loss: 1.5892
Epoch 4/4... Discriminator Loss: 0.6006... Generator Loss: 1.8630
Epoch 4/4... Discriminator Loss: 0.5521... Generator Loss: 1.6314
Epoch 4/4... Discriminator Loss: 0.6140... Generator Loss: 1.4067
Epoch 4/4... Discriminator Loss: 0.6077... Generator Loss: 1.6044
Epoch 4/4... Discriminator Loss: 0.6432... Generator Loss: 1.6516
Epoch 4/4... Discriminator Loss: 0.7829... Generator Loss: 1.0854
Epoch 4/4... Discriminator Loss: 1.2371... Generator Loss: 0.7237
Epoch 4/4... Discriminator Loss: 0.6796... Generator Loss: 1.3462
Epoch 4/4... Discriminator Loss: 0.6057... Generator Loss: 1.4419
Epoch 4/4... Discriminator Loss: 0.4984... Generator Loss: 2.2135
Epoch 4/4... Discriminator Loss: 0.6046... Generator Loss: 1.8814
Epoch 4/4... Discriminator Loss: 1.1010... Generator Loss: 0.7155
Epoch 4/4... Discriminator Loss: 0.9091... Generator Loss: 1.3024
Epoch 4/4... Discriminator Loss: 0.9251... Generator Loss: 1.0044
Epoch 4/4... Discriminator Loss: 0.6923... Generator Loss: 1.2229
Epoch 4/4... Discriminator Loss: 1.2048... Generator Loss: 0.6025
Epoch 4/4... Discriminator Loss: 0.4416... Generator Loss: 2.0296
Epoch 4/4... Discriminator Loss: 0.7166... Generator Loss: 1.4656
Epoch 4/4... Discriminator Loss: 0.9440... Generator Loss: 0.9570
Epoch 4/4... Discriminator Loss: 0.7404... Generator Loss: 1.7963
Epoch 4/4... Discriminator Loss: 0.7882... Generator Loss: 1.0596
Epoch 4/4... Discriminator Loss: 0.5119... Generator Loss: 1.7823
Epoch 4/4... Discriminator Loss: 0.6925... Generator Loss: 1.6006
Epoch 4/4... Discriminator Loss: 0.9239... Generator Loss: 1.2658
Epoch 4/4... Discriminator Loss: 0.8522... Generator Loss: 0.9517
Epoch 4/4... Discriminator Loss: 0.7986... Generator Loss: 1.2027
Epoch 4/4... Discriminator Loss: 0.6399... Generator Loss: 1.7177
Epoch 4/4... Discriminator Loss: 0.5206... Generator Loss: 1.4937
Epoch 4/4... Discriminator Loss: 0.8325... Generator Loss: 1.0727
Epoch 4/4... Discriminator Loss: 0.8129... Generator Loss: 1.8002
Epoch 4/4... Discriminator Loss: 0.7908... Generator Loss: 0.9453
Epoch 4/4... Discriminator Loss: 1.1982... Generator Loss: 2.2396
Epoch 4/4... Discriminator Loss: 0.8585... Generator Loss: 0.9670
Epoch 4/4... Discriminator Loss: 1.0244... Generator Loss: 0.7700
Epoch 4/4... Discriminator Loss: 1.2320... Generator Loss: 1.4292
Epoch 4/4... Discriminator Loss: 0.9796... Generator Loss: 0.9798
Epoch 4/4... Discriminator Loss: 0.8927... Generator Loss: 0.9326
Epoch 4/4... Discriminator Loss: 0.5802... Generator Loss: 1.3885
Epoch 4/4... Discriminator Loss: 0.6355... Generator Loss: 1.5194
Epoch 4/4... Discriminator Loss: 0.8906... Generator Loss: 3.1775
Epoch 4/4... Discriminator Loss: 0.7246... Generator Loss: 2.0196
Epoch 4/4... Discriminator Loss: 0.8840... Generator Loss: 0.9731
Epoch 4/4... Discriminator Loss: 0.6956... Generator Loss: 1.3897
Epoch 4/4... Discriminator Loss: 0.7854... Generator Loss: 1.0623
Epoch 4/4... Discriminator Loss: 0.7626... Generator Loss: 1.1514
Epoch 4/4... Discriminator Loss: 0.6026... Generator Loss: 1.4533
Epoch 4/4... Discriminator Loss: 0.7345... Generator Loss: 1.0417
Epoch 4/4... Discriminator Loss: 0.6484... Generator Loss: 1.4299
Epoch 4/4... Discriminator Loss: 1.2145... Generator Loss: 0.5611
Epoch 4/4... Discriminator Loss: 0.9642... Generator Loss: 1.2679
Epoch 4/4... Discriminator Loss: 0.3909... Generator Loss: 2.0582
Epoch 4/4... Discriminator Loss: 1.0203... Generator Loss: 0.6887
Epoch 4/4... Discriminator Loss: 0.8200... Generator Loss: 1.0166
Epoch 4/4... Discriminator Loss: 0.9451... Generator Loss: 1.1018
Epoch 4/4... Discriminator Loss: 0.9898... Generator Loss: 1.1443
Epoch 4/4... Discriminator Loss: 0.5482... Generator Loss: 1.7936
Epoch 4/4... Discriminator Loss: 0.6632... Generator Loss: 1.5846
Epoch 4/4... Discriminator Loss: 0.7270... Generator Loss: 2.1834
Epoch 4/4... Discriminator Loss: 0.6633... Generator Loss: 1.4869
Epoch 4/4... Discriminator Loss: 0.8187... Generator Loss: 1.0313
Epoch 4/4... Discriminator Loss: 0.8982... Generator Loss: 1.5308
Epoch 4/4... Discriminator Loss: 0.6920... Generator Loss: 1.2337
Epoch 4/4... Discriminator Loss: 0.9725... Generator Loss: 0.7821
Epoch 4/4... Discriminator Loss: 1.1692... Generator Loss: 0.7665
Epoch 4/4... Discriminator Loss: 0.9787... Generator Loss: 0.8952
Epoch 4/4... Discriminator Loss: 0.9756... Generator Loss: 0.9336
Epoch 4/4... Discriminator Loss: 0.7097... Generator Loss: 1.7510
Epoch 4/4... Discriminator Loss: 0.4591... Generator Loss: 2.0011
Epoch 4/4... Discriminator Loss: 1.1776... Generator Loss: 0.8304
Epoch 4/4... Discriminator Loss: 0.8814... Generator Loss: 0.9085
Epoch 4/4... Discriminator Loss: 0.7668... Generator Loss: 0.9356
Epoch 4/4... Discriminator Loss: 0.8399... Generator Loss: 1.2358
Epoch 4/4... Discriminator Loss: 0.6891... Generator Loss: 1.4631
Epoch 4/4... Discriminator Loss: 0.7370... Generator Loss: 1.2702
Epoch 4/4... Discriminator Loss: 0.7867... Generator Loss: 1.2693
Epoch 4/4... Discriminator Loss: 0.8221... Generator Loss: 2.4966
Epoch 4/4... Discriminator Loss: 0.7717... Generator Loss: 1.1020
Epoch 4/4... Discriminator Loss: 0.3998... Generator Loss: 2.4136
Epoch 4/4... Discriminator Loss: 0.6032... Generator Loss: 2.1241
Epoch 4/4... Discriminator Loss: 0.7679... Generator Loss: 1.1695
Epoch 4/4... Discriminator Loss: 0.6145... Generator Loss: 1.6327
Epoch 4/4... Discriminator Loss: 0.7294... Generator Loss: 1.4351
Epoch 4/4... Discriminator Loss: 1.1007... Generator Loss: 2.0544
Epoch 4/4... Discriminator Loss: 0.4505... Generator Loss: 1.8770
Epoch 4/4... Discriminator Loss: 0.5931... Generator Loss: 1.7824
Epoch 4/4... Discriminator Loss: 0.4723... Generator Loss: 1.5581
Epoch 4/4... Discriminator Loss: 0.5803... Generator Loss: 1.4533
Epoch 4/4... Discriminator Loss: 0.7248... Generator Loss: 1.4800
Epoch 4/4... Discriminator Loss: 0.8502... Generator Loss: 1.0315
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